Abstract
Bridges have a significant importance within the transportation system given that their functionality is vital for the economic and social development of countries. Therefore, a high level of safety and serviceability must be achieved to guarantee an operational state of the bridge network. In this regard, it is necessary to track the performance of bridges and obtain indicators to characterize the evolution of structural pathologies over time. In this paper, the time-dependent expected deterioration of bridge networks is investigated by use of Markov chains models. Bridges in a network are likely to share similar environmental conditions but depending on their functional class may be exposed to different loading conditions that diversely affect their structural deterioration over time. Moreover, the deterioration rate is known to increase with time due to aging. Hence, it is useful to identify and divide the bridge network into classes sharing similar deterioration trends in order to obtain a more accurate prediction. To this end, data mining tools such as two-step cluster analysis is applied to a dataset obtained from the National Bridge Inventory (NBI) database, in order to find associations among the bridge characteristics that could contribute to build a more specific degradation model which accurately explains and predicts the future condition of concrete bridges. The results demonstrate a particular deterioration path for each cluster, where it is evidenced that older bridges and those having higher Average Daily Traffic (ADT) deteriorate faster. Therefore, the degradation models developed following the proposed methodology provide a more accurate prediction when compared to a single degradation model without clustering analysis. This more reliable models facilitate the decision process of bridge management systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
AASHTO. (2013). Manual for bridge element inspection. The American Association of State Highway and Transportation Officials (1st ed.).
Almeida, J. O., Teixeira, P. F., & Delgado, R. M. (2015). Life cycle cost optimisation in highway concrete bridges management. Structure and Infrastructure Engineering, 11(10), 1263–1276. https://doi.org/10.1080/15732479.2013.845578.
Jiang, Y., Saito, M., & Shina, K. C. (1988). Bridge performance prediction model using the Markov chain. Journal of the Transportation Research Board, 1180, 25–32.
Jiang, Y. (1990). The development of performance prediction and optimization models for bridge management systems. Purdue University.
Muñoz, Y. F., Paz, A., Hanns De La Fuente-Mella, J. V. F., & Sales, G. M. (2016). Estimating bridge deterioration for small data sets using regression and markov models. World Academy Science International Science Index, Urban Civil Engineering, 10(5).
Jiang, Y. (2010). Application and comparison of regression and Markov chain methods in bridge condition prediction and system benefit optimization. Journal of the Transportation Research Forum, 49(2), 210.
Li, L., Sun, L., & Ning, G. (2014). Deterioration prediction of urban bridges on network level using Markov-chain model. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/728107.
Tolliver, D., & Lu, P. (2011). Analysis of bridge deterioration rates: A case study of the Northern Plains Region. Transportation Research Forum, 50(2), 87–100 [Online]. Available: https://www.trforum.org/journal.
Chin, P. A., Ferris, J. B., & Reid, A. A. (2012). Improving Markov chain models for road profiles simulation via definition of states.
Kobayashi, K., Do, M., & Han, D. (2010). Estimation of Markovian transition probabilities for pavement deterioration forecasting. KSCE Journal of Civil Engineering, 14(3), 343–351. https://doi.org/10.1007/s12205-010-0343-x.
Baik, H.-S., Jeong, H. S. D., & Abraham, D. M. (2006). Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems. Journal of Water Resource Plannning Management, 132:1(15). https://doi.org/10.1061/(ASCE)0733-9496(2006)132.
Santamaria Ariza, M., Zambon, I., Sousa, H. S., Campos e Matos, J. A., & Strauss, A. (2020). Comparison of forecasting models to predict concrete bridge decks performance. Structure Concrete.
Radovic, M., Ghonima, O., & Schumacher, T. (2017). Data mining of bridge concrete deck parameters in the national bridge inventory by two-step cluster analysis. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A Civil Engineering, 3(2), 4016004. https://doi.org/10.1061/AJRUA6.0000889.
Setunge, S., & Hasan, M. S. (2011). Concrete bridge deterioration prediction using Markov chain approach. Digital Library, University of Moratuwa.
Morcous, G. (2006). Performance prediction of bridge deck systems using Markov chains. Journal of Performance of Constructed Facilities, 20(2), 146–155. https://doi.org/10.1061/(ASCE)0887-3828(2006)20:2(146).
Ranjith, S., Setunge, S., Gravina, R., & Venkatesan, S. (2013). Deterioration prediction of timber bridge elements using the Markov chain. Journal of Performance of Constructed Facilities, 27(3), 319–325. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000311.
Tan, P., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Pearson Addison Wesley.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Elsevier.
IBM. (2020). TwoStep cluster analysis. IBM. https://www.ibm.com/support/knowledgecenter/SSLVMB_27.0.0/statistics_casestudies_project_ddita/spss/tutorials/twostepcluster_table.html.
U.S. Department of Transportation Federal Highway Administration, NBI ASCII files—National Bridge Inventory—Bridge Inspection—Safety—Bridges & Structures—Federal Highway Administration.
Cesare, M. A., Santamarina, C., Turkstra, C., & Vanmarcke, H. E. (1992). Modeling bridge deterioration with Markov chains. Journal of Transportation Engineering, 118(6), 1129–1945.
Bacher, J., Wenzig, K., & Vogler, M. (2004). SPSS TwoStep cluster-a first evaluation.
Acknowledgements
This work was partly financed by FEDER funds through the Competitivity Factors Operational Programme—COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633.
This project received funding to carry out this publication of the European Union’s Portugal 2020 research and innovation program under the I&D project “GIIP—Intelligent Management of Port Infrastructures”, with POCI-01-0247-FEDER-039890. The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Moscoso, Y.F.M., Santamaria, M., Sousa, H.S., Matos, J.C. (2021). Stochastic Degradation Model of Concrete Bridges Using Data Mining Tools. In: Matos, J.C., et al. 18th International Probabilistic Workshop. IPW 2021. Lecture Notes in Civil Engineering, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-030-73616-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-73616-3_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73615-6
Online ISBN: 978-3-030-73616-3
eBook Packages: EngineeringEngineering (R0)